Data Engineer - AWS

Requirements:
At least 5-8 years of analytics engineering / data engineering, with a minimum of 2 years of experience in architecting and solutioning.
Demonstrated track record of applying deep technical expertise to diagnose business problems rationally and solve them relentlessly. Note that we're NOT an AI lab doing cutting-edge research; this role demands strategizing, application, and execution.
AI/LLM Readiness: Experience working with Vector Databases, Knowledge Graphs, or creating data layers specifically for Generative AI consumption.
Data Modeling: Strong proficiency in dimensional modeling (Star Schema, Snowflake) and modern data lakehouse table formats (Delta Lake, Iceberg, or Hudi).
Tech-Stack Adoption: Strong command of distributed computing principles. Spark, Hive, BigQuery. Understand DAGs, shuffling, serialization, and partition pruning.
Data Engineering: Strong data engineering experience, implementing large-scale and complex systems.
Deep Tuning Experience: Proven ability to look at a "spill-to-disk" error or a slow stage and know exactly which configuration knob to turn.
Programming: Proficiency in Advanced SQL, Python, R, Spark, and Spark SQL. Good to have Scala.
Familiarity with modern table formats (Delta Lake, Iceberg, Hudi).
The "AI/Modern" Experience:
Experience building data layers specifically for Generative AI (Vector DBs, RAG architectures, or Semantic Layers).
Vibe coding an analytics UI/Dashboard using Lovable/Claude/etc. for POCs, which can then be replicated for implementation
Prepare data for RAG (Retrieval-Augmented Generation) architectures.
Nice to have: BI/metric layer tools (LookML/Transform/MetricFlow/Semantic Layer tools).

Experience
6-10 yrs